An investigation into the performances of the Current state-of-the-art Naive Bayes, Non-Bayesian and Deep Learning Based Classifier for Phishing Detection: A Survey Article Swipe

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Tosin Ige , Christopher Kiekintveld , Aritran Piplai , Amy Waggler , Olukunle Kolade , Bolanle Hafiz Matti · YOU? · · 2024 · Open Access · · DOI: https://doi.org/10.48550/arxiv.2411.16751

Phishing is one of the most effective ways in which cybercriminals get sensitive details such as credentials for online banking, digital wallets, state secrets, and many more from potential victims. They do this by spamming users with malicious URLs with the sole purpose of tricking them into divulging sensitive information which is later used for various cybercrimes. In this research, we did a comprehensive review of current state-of-the-art machine learning and deep learning phishing detection techniques to expose their vulnerabilities and future research direction. For better analysis and observation, we split machine learning techniques into Bayesian, non-Bayesian, and deep learning. We reviewed the most recent advances in Bayesian and non-Bayesian-based classifiers before exploiting their corresponding weaknesses to indicate future research direction. While exploiting weaknesses in both Bayesian and non-Bayesian classifiers, we also compared each performance with a deep learning classifier. For a proper review of deep learning-based classifiers, we looked at Recurrent Neural Networks (RNN), Convolutional Neural Networks (CNN), and Long Short Term Memory Networks (LSTMs). We did an empirical analysis to evaluate the performance of each classifier along with many of the proposed state-of-the-art anti-phishing techniques to identify future research directions, we also made a series of proposals on how the performance of the under-performing algorithm can improved in addition to a two-stage prediction model

Type

preprint

Language

en

Landing Page

http://arxiv.org/abs/2411.16751

PDF

https://arxiv.org/pdf/2411.16751

OA Status

green

Cited By

3

Related Works

10

OpenAlex ID

https://openalex.org/W4404988040

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OpenAlex ID

https://openalex.org/W4404988040

Canonical identifier for this work in OpenAlex

DOI

https://doi.org/10.48550/arxiv.2411.16751

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Title

An investigation into the performances of the Current state-of-the-art Naive Bayes, Non-Bayesian and Deep Learning Based Classifier for Phishing Detection: A Survey

Work title

Type

preprint

OpenAlex work type

Language

en

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Publication year

2024

Year of publication

Publication date

2024-11-24

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Authors

Tosin Ige, Christopher Kiekintveld, Aritran Piplai, Amy Waggler, Olukunle Kolade, Bolanle Hafiz Matti

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Landing page

https://arxiv.org/abs/2411.16751

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PDF URL

https://arxiv.org/pdf/2411.16751

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Open access

Yes

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OA status

green

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OA URL

https://arxiv.org/pdf/2411.16751

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Concepts

Naive Bayes classifier, Artificial intelligence, Deep learning, Computer science, Machine learning, Bayesian probability, Classifier (UML), Bayes' theorem, Pattern recognition (psychology), Support vector machine

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Cited by

3

Total citation count in OpenAlex

Citations by year (recent)

2025: 3

Per-year citation counts (last 5 years)

Related works (count)

10

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